Genetic and phenotypic traits in livestock

This study compares hidden Markov model-based imputation methods in animal breeding, finding that pre-phasing with Eagle2 or HAPI-UR and haploid imputation with minimac3 or IMPUTE2 is faster and more accurate than diploid imputation.

This study compares liver proteomic profiles of two pig breeds using label-free LC-MS, identifying 25 differentially expressed proteins linked to metabolic differences.

This paper applies random regression models to predict longitudinal shoot growth traits in rice using genomic data, showing improved accuracy over single time point predictions.

DeepGS predicts phenotypes from genotypes using deep learning, achieving higher accuracy than conventional methods and RR-BLUP across multiple traits.

This paper develops a method to integrate individual-level genomic data and summary statistics across populations to improve allele substitution effect estimates and genomic predictions.

The paper identifies two mutations affecting prolificacy in two sheep breeds and estimates their allele effects on litter size.

This study identifies novel genes and variations associated with glycolytic potential in pigs using RNA-seq and SNP analysis across different GP groups.

This study improves genomic prediction models for maize hybrids by incorporating dominance effects and genotype-by-environment interactions, enhancing accuracy for grain yield and moisture.

The paper identifies bovine sQTLs associated with RNA splicing variation and shows they are shared across tissues and linked to complex traits.

The paper presents the draft genome and transcriptome maps of the Yeonsan Ogye chicken breed, highlighting structural variations and tissue-specific gene expression patterns.

This study estimates deleterious mutation burden in sorghum and assesses its impact on agronomic traits, finding moderate genetic variance explanation but limited improvement in breeding accuracy due to linkage.

The sommer R package gains a multivariate solver: several random effects at once, with covariance structures set per effect and between traits, for genome-assisted breeding prediction.

This paper compares optimal cross selection with truncation selection in plant breeding programs, showing optimal cross selection achieves higher long-term genetic gain by improving efficiency and reducing genetic drift.

The paper identifies exercise-related positively selected genes in Abaga and Wushen horses, highlighting chromosome 4's role in athletic performance and suggesting molecular basis for breeding.

The paper identifies a genomic region containing REC8 and RNF212B associated with recombination rate variation in red deer, showing heritability and conservation across mammals.

This study uses genomic prediction with genotype by environment interaction to improve rice breeding for adaptation to alternate wetting and drying, showing multi-environment models outperform single-environment models.

The paper analyzes runs of homozygosity (ROH) in domestic dogs to assess their contribution to inbreeding depression, finding that both long and short ROH contribute to autozygosity at deleterious loci.

AlphaMate optimizes selection, diversity maintenance, and mate allocation in breeding programs using multi-objective evolutionary algorithms, improving genetic gain efficiency in plants and animals.

The paper estimates genomic heritability in sweet cherry, finding non-additive genetic variance is significant for key traits, and highlights the importance of including these components in breeding models.

The paper identifies QTL associated with adaptive traits in Scots pine using a three-generation pedigree, finding 62 significant QTL with variance explained up to 18.9%.

This study evaluates bias and accuracy in genomic breeding value prediction under selection scenarios using different statistical models and finds that SSGBLUP with proper scaling reduces bias and improves accuracy.

The paper identifies rare variants associated with body length and girth in cattle through whole-exome sequencing and functional analysis.

The paper identifies differentially expressed small RNAs in pigs with divergent backfat deposition and predicts their target genes to understand fat trait regulation.

The paper identifies three SNPs associated with carcass traits in chickens by analyzing predicted deleterious variants within QTL regions.

The paper determines the minimum SNP density needed for accurate parentage verification in cattle and introduces a genomic sample QC pipeline for large cattle datasets.

This paper develops and evaluates broad-based multi-trait Bayesian regression methods for genomic prediction, showing improved accuracy over single-trait and restricted multi-trait approaches.

This study identifies a genomic region associated with resistance to Ostreid Herpesvirus in Pacific oysters using a genome-wide association study and linkage mapping.

This study uses genome-wide SNP data to assess genetic diversity and conservation status of Kerry cattle, revealing reduced effective population size and increased inbreeding.

The paper identifies a SNP associated with resistance to P. salmonis in coho salmon and shows genomic selection models perform similarly to pedigree-based methods.

The paper evaluates prediction strategies for hybrid rice traits using various omic datasets and methods, finding genomic-metabolomic combinations and BLUP as most effective.